Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations8000
Missing cells10080
Missing cells (%)7.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 MiB
Average record size in memory275.2 B

Variable types

Numeric10
Text1
Categorical6

Alerts

Age is highly overall correlated with CreditScore_Age_RatioHigh correlation
Age_Active_Interaction is highly overall correlated with IsActiveMemberHigh correlation
Balance is highly overall correlated with BalancePerProductHigh correlation
BalancePerProduct is highly overall correlated with BalanceHigh correlation
CreditScore_Age_Ratio is highly overall correlated with AgeHigh correlation
IsActiveMember is highly overall correlated with Age_Active_InteractionHigh correlation
Tenure is highly overall correlated with Tenure_Age_RatioHigh correlation
Tenure_Age_Ratio is highly overall correlated with TenureHigh correlation
Surname has 409 (5.1%) missing valuesMissing
CreditScore has 1641 (20.5%) missing valuesMissing
Balance has 1615 (20.2%) missing valuesMissing
NumOfProducts has 976 (12.2%) missing valuesMissing
HasCrCard has 375 (4.7%) missing valuesMissing
EstimatedSalary has 832 (10.4%) missing valuesMissing
BalancePerProduct has 2591 (32.4%) missing valuesMissing
CreditScore_Age_Ratio has 1641 (20.5%) missing valuesMissing
CustomerId has unique valuesUnique
Tenure has 334 (4.2%) zerosZeros
Balance has 2283 (28.5%) zerosZeros
BalancePerProduct has 2283 (28.5%) zerosZeros
Age_Active_Interaction has 3892 (48.6%) zerosZeros
Tenure_Age_Ratio has 334 (4.2%) zerosZeros

Reproduction

Analysis started2025-11-22 10:46:21.899852
Analysis finished2025-11-22 10:46:41.917026
Duration20.02 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

CustomerId
Real number (ℝ)

Unique 

Distinct8000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15691188
Minimum15565701
Maximum15815690
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-11-22T11:46:42.178821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15565701
5-th percentile15579208
Q115628960
median15691115
Q315753728
95-th percentile15802585
Maximum15815690
Range249989
Interquartile range (IQR)124768

Descriptive statistics

Standard deviation71872.267
Coefficient of variation (CV)0.0045804221
Kurtosis-1.1991968
Mean15691188
Median Absolute Deviation (MAD)62273
Skewness-0.0013115379
Sum1.2552951 × 1011
Variance5.1656227 × 109
MonotonicityNot monotonic
2025-11-22T11:46:42.542720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
158112611
 
< 0.1%
155674311
 
< 0.1%
157047951
 
< 0.1%
155927731
 
< 0.1%
156862191
 
< 0.1%
157637471
 
< 0.1%
156479751
 
< 0.1%
157349701
 
< 0.1%
156652831
 
< 0.1%
156078271
 
< 0.1%
Other values (7990)7990
99.9%
ValueCountFrequency (%)
155657011
< 0.1%
155657061
< 0.1%
155657961
< 0.1%
155658061
< 0.1%
155658791
< 0.1%
155658911
< 0.1%
155659961
< 0.1%
155660911
< 0.1%
155661111
< 0.1%
155661391
< 0.1%
ValueCountFrequency (%)
158156901
< 0.1%
158156601
< 0.1%
158156561
< 0.1%
158156281
< 0.1%
158156261
< 0.1%
158155601
< 0.1%
158155301
< 0.1%
158154901
< 0.1%
158154431
< 0.1%
158154281
< 0.1%

Surname
Text

Missing 

Distinct2523
Distinct (%)33.2%
Missing409
Missing (%)5.1%
Memory size423.8 KiB
2025-11-22T11:46:43.125328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length23
Median length16
Mean length6.4327493
Min length2

Characters and Unicode

Total characters48831
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1338 ?
Unique (%)17.6%

Sample

1st rowBoone
2nd rowShe
3rd rowKerr
4th rowLoggia
5th rowChiekwugo
ValueCountFrequency (%)
scott25
 
0.3%
lo25
 
0.3%
brown23
 
0.3%
martin23
 
0.3%
smith23
 
0.3%
yeh22
 
0.3%
shih21
 
0.3%
maclean18
 
0.2%
johnson18
 
0.2%
genovese18
 
0.2%
Other values (2520)7415
97.2%
2025-11-22T11:46:43.698448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a4409
 
9.0%
e4346
 
8.9%
n3978
 
8.1%
o3745
 
7.7%
i3398
 
7.0%
r2737
 
5.6%
l2185
 
4.5%
s1959
 
4.0%
u1913
 
3.9%
h1646
 
3.4%
Other values (45)18515
37.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)48831
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a4409
 
9.0%
e4346
 
8.9%
n3978
 
8.1%
o3745
 
7.7%
i3398
 
7.0%
r2737
 
5.6%
l2185
 
4.5%
s1959
 
4.0%
u1913
 
3.9%
h1646
 
3.4%
Other values (45)18515
37.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)48831
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a4409
 
9.0%
e4346
 
8.9%
n3978
 
8.1%
o3745
 
7.7%
i3398
 
7.0%
r2737
 
5.6%
l2185
 
4.5%
s1959
 
4.0%
u1913
 
3.9%
h1646
 
3.4%
Other values (45)18515
37.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)48831
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a4409
 
9.0%
e4346
 
8.9%
n3978
 
8.1%
o3745
 
7.7%
i3398
 
7.0%
r2737
 
5.6%
l2185
 
4.5%
s1959
 
4.0%
u1913
 
3.9%
h1646
 
3.4%
Other values (45)18515
37.9%

CreditScore
Real number (ℝ)

Missing 

Distinct448
Distinct (%)7.0%
Missing1641
Missing (%)20.5%
Infinite0
Infinite (%)0.0%
Mean661.23526
Minimum350
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-11-22T11:46:43.860379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum350
5-th percentile500
Q1595
median664
Q3729
95-th percentile824
Maximum850
Range500
Interquartile range (IQR)134

Descriptive statistics

Standard deviation95.876126
Coefficient of variation (CV)0.14499548
Kurtosis-0.39748144
Mean661.23526
Median Absolute Deviation (MAD)67
Skewness-0.1203844
Sum4204795
Variance9192.2315
MonotonicityNot monotonic
2025-11-22T11:46:44.053496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850187
 
2.3%
70542
 
0.5%
67842
 
0.5%
68440
 
0.5%
66739
 
0.5%
68738
 
0.5%
65536
 
0.4%
68634
 
0.4%
65234
 
0.4%
63333
 
0.4%
Other values (438)5834
72.9%
(Missing)1641
 
20.5%
ValueCountFrequency (%)
3502
< 0.1%
3511
< 0.1%
3581
< 0.1%
3631
< 0.1%
3651
< 0.1%
3671
< 0.1%
3731
< 0.1%
3762
< 0.1%
3831
< 0.1%
4011
< 0.1%
ValueCountFrequency (%)
850187
2.3%
8497
 
0.1%
8481
 
< 0.1%
8474
 
0.1%
8465
 
0.1%
8454
 
0.1%
8446
 
0.1%
8432
 
< 0.1%
8426
 
0.1%
84110
 
0.1%

Geography
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size429.8 KiB
France
4030 
Germany
1986 
Spain
1984 

Length

Max length7
Median length6
Mean length6.00025
Min length5

Characters and Unicode

Total characters48002
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGermany
2nd rowFrance
3rd rowSpain
4th rowGermany
5th rowFrance

Common Values

ValueCountFrequency (%)
France4030
50.4%
Germany1986
24.8%
Spain1984
24.8%

Length

2025-11-22T11:46:44.238587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-22T11:46:44.371681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
france4030
50.4%
germany1986
24.8%
spain1984
24.8%

Most occurring characters

ValueCountFrequency (%)
n8000
16.7%
a8000
16.7%
r6016
12.5%
e6016
12.5%
F4030
8.4%
c4030
8.4%
G1986
 
4.1%
m1986
 
4.1%
y1986
 
4.1%
S1984
 
4.1%
Other values (2)3968
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)48002
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n8000
16.7%
a8000
16.7%
r6016
12.5%
e6016
12.5%
F4030
8.4%
c4030
8.4%
G1986
 
4.1%
m1986
 
4.1%
y1986
 
4.1%
S1984
 
4.1%
Other values (2)3968
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)48002
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n8000
16.7%
a8000
16.7%
r6016
12.5%
e6016
12.5%
F4030
8.4%
c4030
8.4%
G1986
 
4.1%
m1986
 
4.1%
y1986
 
4.1%
S1984
 
4.1%
Other values (2)3968
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)48002
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n8000
16.7%
a8000
16.7%
r6016
12.5%
e6016
12.5%
F4030
8.4%
c4030
8.4%
G1986
 
4.1%
m1986
 
4.1%
y1986
 
4.1%
S1984
 
4.1%
Other values (2)3968
8.3%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size421.3 KiB
Male
4384 
Female
3616 

Length

Max length6
Median length4
Mean length4.904
Min length4

Characters and Unicode

Total characters39232
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male4384
54.8%
Female3616
45.2%

Length

2025-11-22T11:46:44.861439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-22T11:46:44.973121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male4384
54.8%
female3616
45.2%

Most occurring characters

ValueCountFrequency (%)
e11616
29.6%
a8000
20.4%
l8000
20.4%
M4384
 
11.2%
F3616
 
9.2%
m3616
 
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)39232
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e11616
29.6%
a8000
20.4%
l8000
20.4%
M4384
 
11.2%
F3616
 
9.2%
m3616
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)39232
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e11616
29.6%
a8000
20.4%
l8000
20.4%
M4384
 
11.2%
F3616
 
9.2%
m3616
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)39232
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e11616
29.6%
a8000
20.4%
l8000
20.4%
M4384
 
11.2%
F3616
 
9.2%
m3616
 
9.2%

Age
Real number (ℝ)

High correlation 

Distinct69
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.935
Minimum18
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-11-22T11:46:45.117406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile25
Q132
median37
Q344
95-th percentile60
Maximum92
Range74
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.381389
Coefficient of variation (CV)0.26663386
Kurtosis1.3939202
Mean38.935
Median Absolute Deviation (MAD)6
Skewness1.0060536
Sum311480
Variance107.77325
MonotonicityNot monotonic
2025-11-22T11:46:45.314098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35395
 
4.9%
37388
 
4.9%
38386
 
4.8%
34363
 
4.5%
33360
 
4.5%
36357
 
4.5%
40344
 
4.3%
39343
 
4.3%
32336
 
4.2%
31303
 
3.8%
Other values (59)4425
55.3%
ValueCountFrequency (%)
1815
 
0.2%
1921
 
0.3%
2031
 
0.4%
2143
 
0.5%
2262
 
0.8%
2374
0.9%
24107
1.3%
25109
1.4%
26164
2.1%
27166
2.1%
ValueCountFrequency (%)
921
 
< 0.1%
881
 
< 0.1%
841
 
< 0.1%
831
 
< 0.1%
821
 
< 0.1%
813
 
< 0.1%
803
 
< 0.1%
793
 
< 0.1%
785
0.1%
7710
0.1%

Tenure
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.013875
Minimum0
Maximum10
Zeros334
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-11-22T11:46:45.472266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q38
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8888104
Coefficient of variation (CV)0.57616323
Kurtosis-1.1575913
Mean5.013875
Median Absolute Deviation (MAD)2
Skewness0.0064856405
Sum40111
Variance8.3452256
MonotonicityNot monotonic
2025-11-22T11:46:45.600468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8837
10.5%
1832
10.4%
2822
10.3%
5819
10.2%
7813
10.2%
4810
10.1%
3790
9.9%
6779
9.7%
9771
9.6%
10393
4.9%
ValueCountFrequency (%)
0334
 
4.2%
1832
10.4%
2822
10.3%
3790
9.9%
4810
10.1%
5819
10.2%
6779
9.7%
7813
10.2%
8837
10.5%
9771
9.6%
ValueCountFrequency (%)
10393
4.9%
9771
9.6%
8837
10.5%
7813
10.2%
6779
9.7%
5819
10.2%
4810
10.1%
3790
9.9%
2822
10.3%
1832
10.4%

Balance
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct4103
Distinct (%)64.3%
Missing1615
Missing (%)20.2%
Infinite0
Infinite (%)0.0%
Mean76929.669
Minimum0
Maximum250898.09
Zeros2283
Zeros (%)28.5%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-11-22T11:46:45.773658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median98009.13
Q3127660.46
95-th percentile162851.45
Maximum250898.09
Range250898.09
Interquartile range (IQR)127660.46

Descriptive statistics

Standard deviation62292.133
Coefficient of variation (CV)0.80972834
Kurtosis-1.4803442
Mean76929.669
Median Absolute Deviation (MAD)45635.03
Skewness-0.15278862
Sum4.9119594 × 108
Variance3.8803099 × 109
MonotonicityNot monotonic
2025-11-22T11:46:45.971638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02283
28.5%
106234.021
 
< 0.1%
62389.031
 
< 0.1%
100421.11
 
< 0.1%
133446.341
 
< 0.1%
87271.411
 
< 0.1%
52192.081
 
< 0.1%
98548.621
 
< 0.1%
121376.151
 
< 0.1%
55053.621
 
< 0.1%
Other values (4093)4093
51.2%
(Missing)1615
 
20.2%
ValueCountFrequency (%)
02283
28.5%
3768.691
 
< 0.1%
12459.191
 
< 0.1%
14262.81
 
< 0.1%
16893.591
 
< 0.1%
27288.431
 
< 0.1%
28082.951
 
< 0.1%
28649.641
 
< 0.1%
29602.081
 
< 0.1%
33563.951
 
< 0.1%
ValueCountFrequency (%)
250898.091
< 0.1%
238387.561
< 0.1%
216109.881
< 0.1%
212778.21
< 0.1%
212692.971
< 0.1%
210433.081
< 0.1%
209490.211
< 0.1%
207034.961
< 0.1%
206868.781
< 0.1%
206329.651
< 0.1%

NumOfProducts
Categorical

Missing 

Distinct4
Distinct (%)0.1%
Missing976
Missing (%)12.2%
Memory size410.2 KiB
1.0
3484 
2.0
3323 
3.0
 
183
4.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21072
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.03484
43.5%
2.03323
41.5%
3.0183
 
2.3%
4.034
 
0.4%
(Missing)976
 
12.2%

Length

2025-11-22T11:46:46.153974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-22T11:46:46.271210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.03484
49.6%
2.03323
47.3%
3.0183
 
2.6%
4.034
 
0.5%

Most occurring characters

ValueCountFrequency (%)
.7024
33.3%
07024
33.3%
13484
16.5%
23323
15.8%
3183
 
0.9%
434
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)21072
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.7024
33.3%
07024
33.3%
13484
16.5%
23323
15.8%
3183
 
0.9%
434
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)21072
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.7024
33.3%
07024
33.3%
13484
16.5%
23323
15.8%
3183
 
0.9%
434
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)21072
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.7024
33.3%
07024
33.3%
13484
16.5%
23323
15.8%
3183
 
0.9%
434
 
0.2%

HasCrCard
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing375
Missing (%)4.7%
Memory size407.8 KiB
1.0
5388 
0.0
2237 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters22875
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.05388
67.3%
0.02237
28.0%
(Missing)375
 
4.7%

Length

2025-11-22T11:46:46.401764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-22T11:46:46.490947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.05388
70.7%
0.02237
29.3%

Most occurring characters

ValueCountFrequency (%)
09862
43.1%
.7625
33.3%
15388
23.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)22875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
09862
43.1%
.7625
33.3%
15388
23.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)22875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
09862
43.1%
.7625
33.3%
15388
23.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)22875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
09862
43.1%
.7625
33.3%
15388
23.6%

IsActiveMember
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
1
4108 
0
3892 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
14108
51.3%
03892
48.6%

Length

2025-11-22T11:46:46.614848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-22T11:46:46.704192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
14108
51.3%
03892
48.6%

Most occurring characters

ValueCountFrequency (%)
14108
51.3%
03892
48.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)8000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
14108
51.3%
03892
48.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
14108
51.3%
03892
48.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
14108
51.3%
03892
48.6%

EstimatedSalary
Real number (ℝ)

Missing 

Distinct7167
Distinct (%)> 99.9%
Missing832
Missing (%)10.4%
Infinite0
Infinite (%)0.0%
Mean100057.17
Minimum11.58
Maximum199992.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-11-22T11:46:46.852516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11.58
5-th percentile9817.0005
Q151545.353
median100129.08
Q3149216.32
95-th percentile189984.9
Maximum199992.48
Range199980.9
Interquartile range (IQR)97670.967

Descriptive statistics

Standard deviation57441.733
Coefficient of variation (CV)0.57408915
Kurtosis-1.1790461
Mean100057.17
Median Absolute Deviation (MAD)48801.565
Skewness0.002925442
Sum7.1720976 × 108
Variance3.2995527 × 109
MonotonicityNot monotonic
2025-11-22T11:46:47.058287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24924.922
 
< 0.1%
129956.131
 
< 0.1%
46172.471
 
< 0.1%
57553.981
 
< 0.1%
125979.361
 
< 0.1%
116124.281
 
< 0.1%
121440.81
 
< 0.1%
110932.241
 
< 0.1%
130686.591
 
< 0.1%
146371.721
 
< 0.1%
Other values (7157)7157
89.5%
(Missing)832
 
10.4%
ValueCountFrequency (%)
11.581
< 0.1%
90.071
< 0.1%
91.751
< 0.1%
96.271
< 0.1%
142.811
< 0.1%
178.191
< 0.1%
236.451
< 0.1%
247.361
< 0.1%
287.991
< 0.1%
332.811
< 0.1%
ValueCountFrequency (%)
199992.481
< 0.1%
199970.741
< 0.1%
199909.321
< 0.1%
199862.751
< 0.1%
199857.471
< 0.1%
199841.321
< 0.1%
199693.841
< 0.1%
199674.831
< 0.1%
199661.51
< 0.1%
199644.21
< 0.1%

Exited
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
0
6370 
1
1630 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
06370
79.6%
11630
 
20.4%

Length

2025-11-22T11:46:47.233889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-22T11:46:47.323030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
06370
79.6%
11630
 
20.4%

Most occurring characters

ValueCountFrequency (%)
06370
79.6%
11630
 
20.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)8000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
06370
79.6%
11630
 
20.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
06370
79.6%
11630
 
20.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
06370
79.6%
11630
 
20.4%

BalancePerProduct
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct3127
Distinct (%)57.8%
Missing2591
Missing (%)32.4%
Infinite0
Infinite (%)0.0%
Mean55724.199
Minimum0
Maximum238387.56
Zeros2283
Zeros (%)28.5%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-11-22T11:46:47.468336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median51622.43
Q3105627.95
95-th percentile149998.98
Maximum238387.56
Range238387.56
Interquartile range (IQR)105627.95

Descriptive statistics

Standard deviation56051.585
Coefficient of variation (CV)1.0058751
Kurtosis-1.1456221
Mean55724.199
Median Absolute Deviation (MAD)51622.43
Skewness0.44707922
Sum3.0141219 × 108
Variance3.1417802 × 109
MonotonicityNot monotonic
2025-11-22T11:46:47.661153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02283
28.5%
63771.5551
 
< 0.1%
78778.491
 
< 0.1%
55684.8951
 
< 0.1%
128252.661
 
< 0.1%
61985.7551
 
< 0.1%
56596.7551
 
< 0.1%
82407.511
 
< 0.1%
49131.731
 
< 0.1%
96296.781
 
< 0.1%
Other values (3117)3117
39.0%
(Missing)2591
32.4%
ValueCountFrequency (%)
02283
28.5%
1884.3451
 
< 0.1%
11717.60751
 
< 0.1%
12459.191
 
< 0.1%
14590.886671
 
< 0.1%
15462.721
 
< 0.1%
16502.931
 
< 0.1%
16781.9751
 
< 0.1%
16893.591
 
< 0.1%
16970.403331
 
< 0.1%
ValueCountFrequency (%)
238387.561
< 0.1%
216109.881
< 0.1%
2059621
< 0.1%
204510.941
< 0.1%
204223.031
< 0.1%
203715.151
< 0.1%
202904.641
< 0.1%
200724.961
< 0.1%
199689.491
< 0.1%
197436.821
< 0.1%

Age_Active_Interaction
Real number (ℝ)

High correlation  Zeros 

Distinct70
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.44525
Minimum0
Maximum92
Zeros3892
Zeros (%)48.6%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-11-22T11:46:47.846420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23
Q338
95-th percentile57
Maximum92
Range92
Interquartile range (IQR)38

Descriptive statistics

Standard deviation21.56909
Coefficient of variation (CV)1.0549683
Kurtosis-1.0839953
Mean20.44525
Median Absolute Deviation (MAD)23
Skewness0.44091076
Sum163562
Variance465.22566
MonotonicityNot monotonic
2025-11-22T11:46:48.026259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03892
48.6%
35202
 
2.5%
37199
 
2.5%
32191
 
2.4%
38189
 
2.4%
36179
 
2.2%
34176
 
2.2%
33169
 
2.1%
40156
 
1.9%
31156
 
1.9%
Other values (60)2491
31.1%
ValueCountFrequency (%)
03892
48.6%
189
 
0.1%
1912
 
0.1%
2014
 
0.2%
2126
 
0.3%
2233
 
0.4%
2336
 
0.4%
2456
 
0.7%
2557
 
0.7%
2684
 
1.1%
ValueCountFrequency (%)
921
 
< 0.1%
881
 
< 0.1%
841
 
< 0.1%
831
 
< 0.1%
821
 
< 0.1%
813
 
< 0.1%
803
 
< 0.1%
793
 
< 0.1%
785
0.1%
779
0.1%

CreditScore_Age_Ratio
Real number (ℝ)

High correlation  Missing 

Distinct4388
Distinct (%)69.0%
Missing1641
Missing (%)20.5%
Infinite0
Infinite (%)0.0%
Mean18.100687
Minimum5.8295455
Maximum46.888889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-11-22T11:46:48.208993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.8295455
5-th percentile10.384389
Q114.340213
median17.5
Q321.257604
95-th percentile27.965385
Maximum46.888889
Range41.059343
Interquartile range (IQR)6.9173909

Descriptive statistics

Standard deviation5.3574012
Coefficient of variation (CV)0.29597779
Kurtosis0.9190589
Mean18.100687
Median Absolute Deviation (MAD)3.4117647
Skewness0.72450053
Sum115102.27
Variance28.701748
MonotonicityNot monotonic
2025-11-22T11:46:48.387740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1718
 
0.2%
2017
 
0.2%
1813
 
0.2%
21.2512
 
0.1%
21.7948717912
 
0.1%
2511
 
0.1%
23.6111111111
 
0.1%
27.4193548411
 
0.1%
1611
 
0.1%
26.562510
 
0.1%
Other values (4378)6233
77.9%
(Missing)1641
 
20.5%
ValueCountFrequency (%)
5.8295454551
< 0.1%
5.8333333331
< 0.1%
6.1126760561
< 0.1%
6.1578947371
< 0.1%
6.229729731
< 0.1%
6.3928571431
< 0.1%
6.5151515151
< 0.1%
6.7662337661
< 0.1%
6.8133333331
< 0.1%
6.8219178081
< 0.1%
ValueCountFrequency (%)
46.888888891
< 0.1%
44.777777781
< 0.1%
44.736842111
< 0.1%
42.51
< 0.1%
41.751
< 0.1%
41.684210531
< 0.1%
41.105263161
< 0.1%
40.105263161
< 0.1%
39.51
< 0.1%
39.222222221
< 0.1%

Tenure_Age_Ratio
Real number (ℝ)

High correlation  Zeros 

Distinct401
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13776189
Minimum0
Maximum0.55555556
Zeros334
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-11-22T11:46:48.554553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01754386
Q10.06514681
median0.13043478
Q30.2
95-th percentile0.29166667
Maximum0.55555556
Range0.55555556
Interquartile range (IQR)0.13485319

Descriptive statistics

Standard deviation0.088997122
Coefficient of variation (CV)0.64602137
Kurtosis-0.084074295
Mean0.13776189
Median Absolute Deviation (MAD)0.067934783
Skewness0.54568393
Sum1102.0951
Variance0.0079204878
MonotonicityNot monotonic
2025-11-22T11:46:48.743683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0334
 
4.2%
0.2151
 
1.9%
0.1666666667133
 
1.7%
0.1428571429117
 
1.5%
0.25105
 
1.3%
0.125104
 
1.3%
0.153846153886
 
1.1%
0.184
 
1.1%
0.111111111170
 
0.9%
0.0833333333368
 
0.9%
Other values (391)6748
84.4%
ValueCountFrequency (%)
0334
4.2%
0.010869565221
 
< 0.1%
0.012987012991
 
< 0.1%
0.013333333331
 
< 0.1%
0.013513513511
 
< 0.1%
0.013698630142
 
< 0.1%
0.013888888892
 
< 0.1%
0.014084507041
 
< 0.1%
0.014285714292
 
< 0.1%
0.014492753622
 
< 0.1%
ValueCountFrequency (%)
0.55555555561
 
< 0.1%
0.52
 
< 0.1%
0.47619047622
 
< 0.1%
0.47368421052
 
< 0.1%
0.45454545453
 
< 0.1%
0.453
 
< 0.1%
0.44444444442
 
< 0.1%
0.43478260879
0.1%
0.42857142863
 
< 0.1%
0.42105263162
 
< 0.1%

Interactions

2025-11-22T11:46:39.430611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:24.558066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:26.127667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:27.616796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:29.242517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:31.042810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:32.628557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:34.600787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:36.198792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:37.945698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:39.578005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:24.740173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:26.260402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:27.782405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:29.381430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:31.214887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:32.801690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:34.750271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:36.346524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:38.093271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:39.731329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:24.879393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:26.401186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:27.932902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:29.829292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:31.362255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:32.967765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:34.900970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:36.497804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:38.218046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:39.897537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:25.036639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:26.557496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:28.118865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:29.979930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:31.514842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:33.305599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:35.068507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:36.641208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:38.365769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:40.047653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:25.186955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:26.709570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:28.277071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:30.132574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:31.665992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:33.543125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:35.230267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:36.785552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:38.505505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:40.227723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:25.343233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:26.865851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:28.447212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:30.281647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:31.816472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:33.755847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:35.383408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:36.917718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:38.647631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:40.397413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:25.497051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:27.035573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:28.618119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:30.447731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:31.998317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:33.934939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:35.550200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:37.071698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:38.815500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:40.548189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:25.661489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:27.184067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:28.785491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:30.598046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:32.164329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:34.107358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:35.711014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:37.229012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:38.968772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:40.699466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:25.811407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:27.328126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:28.928114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:30.748049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:32.330194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:34.270245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:35.868232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:37.375882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:39.136172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:40.851357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:25.957186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:27.467398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:29.080217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:30.885810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:32.483334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:34.435182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:36.020434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:37.787810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T11:46:39.278498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-22T11:46:48.915671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeAge_Active_InteractionBalanceBalancePerProductCreditScoreCreditScore_Age_RatioCustomerIdEstimatedSalaryExitedGenderGeographyHasCrCardIsActiveMemberNumOfProductsTenureTenure_Age_Ratio
Age1.0000.3280.0260.041-0.007-0.8450.008-0.0010.3730.0200.0470.0180.1450.097-0.010-0.348
Age_Active_Interaction0.3281.0000.0060.0070.014-0.282-0.000-0.0160.2380.0350.0220.0160.9990.052-0.031-0.151
Balance0.0260.0061.0000.921-0.005-0.027-0.0150.0190.1430.0000.3240.0440.0000.235-0.004-0.013
BalancePerProduct0.0410.0070.9211.000-0.015-0.043-0.0150.0110.1730.0330.3470.0000.0320.440-0.014-0.028
CreditScore-0.0070.014-0.005-0.0151.0000.4990.018-0.0040.0810.0130.0030.0000.0490.044-0.0020.000
CreditScore_Age_Ratio-0.845-0.282-0.027-0.0430.4991.0000.0050.0030.2810.0040.0420.0000.1000.0700.0060.295
CustomerId0.008-0.000-0.015-0.0150.0180.0051.0000.0080.0320.0000.0100.0000.0170.000-0.025-0.029
EstimatedSalary-0.001-0.0160.0190.011-0.0040.0030.0081.0000.0000.0160.0080.0210.0170.0130.0080.010
Exited0.3730.2380.1430.1730.0810.2810.0320.0001.0000.1050.1730.0030.1510.3870.0320.130
Gender0.0200.0350.0000.0330.0130.0040.0000.0160.1051.0000.0160.0000.0250.0430.0090.000
Geography0.0470.0220.3240.3470.0030.0420.0100.0080.1730.0161.0000.0140.0030.0610.0230.018
HasCrCard0.0180.0160.0440.0000.0000.0000.0000.0210.0030.0000.0141.0000.0000.0000.0390.023
IsActiveMember0.1450.9990.0000.0320.0490.1000.0170.0170.1510.0250.0030.0001.0000.0400.0270.058
NumOfProducts0.0970.0520.2350.4400.0440.0700.0000.0130.3870.0430.0610.0000.0401.0000.0350.032
Tenure-0.010-0.031-0.004-0.014-0.0020.006-0.0250.0080.0320.0090.0230.0390.0270.0351.0000.922
Tenure_Age_Ratio-0.348-0.151-0.013-0.0280.0000.295-0.0290.0100.1300.0000.0180.0230.0580.0320.9221.000

Missing values

2025-11-22T11:46:41.131577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-22T11:46:41.383370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-22T11:46:41.708906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CustomerIdSurnameCreditScoreGeographyGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExitedBalancePerProductAge_Active_InteractionCreditScore_Age_RatioTenure_Age_Ratio
015759244Boone687.0GermanyMale44895368.142.01.011787.85047684.0704415.6136360.181818
115725997She660.0FranceFemale356100768.771.01.0019199.610100768.770018.8571430.171429
215724296Kerr684.0SpainMale412119782.722.00.00120284.67059891.360016.6829270.048780
315636820Loggia725.0GermanyMale408104149.661.01.0062027.900104149.660018.1250000.200000
415744529Chiekwugo510.0FranceMale6380.002.01.01115291.8600.000638.0952380.126984
515763907Watts820.0FranceFemale391104614.291.01.0061538.431104614.290021.0256410.025641
615671800Robinson688.0FranceMale208137624.402.01.01197582.79068812.2002034.4000000.400000
715567383Slone678.0GermanyFemale44298009.132.00.0131384.86049004.5654415.4090910.045455
815777179EllisNaNFranceMale359NaN2.00.01NaN0NaN35NaN0.257143
915650391NaN633.0FranceFemale297169988.351.01.004272.000169988.350021.8275860.241379
CustomerIdSurnameCreditScoreGeographyGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExitedBalancePerProductAge_Active_InteractionCreditScore_Age_RatioTenure_Age_Ratio
799015775750Yao686.0FranceMale379134560.621.01.0027596.390134560.62018.5405410.243243
799115735878Law850.0GermanyFemale4710134381.52NaN0.0026812.891NaN018.0851060.212766
799215737509Morrison850.0SpainMale348NaN1.00.00NaN0NaN025.0000000.235294
799315782089MullenNaNFranceMale336NaN1.01.0058458.260NaN0NaN0.181818
799415663921Pisani429.0FranceMale6070.002.01.01163691.4800.00607.1500000.116667
799515628303Thurgood738.0SpainMale3530.001.01.0115650.7300.003521.0857140.085714
799615699225Pirozzi757.0FranceMale4600.002.01.0037460.0500.00016.4565220.000000
799715612525PrestonNaNFranceFemale571NaN1.00.00131372.381NaN0NaN0.017544
799815724321Baresi516.0GermanyFemale479128298.741.00.00149614.171128298.74010.9787230.191489
799915578761CunninghamNaNSpainFemale426129634.25NaN1.01177683.021NaN42NaN0.142857